A variety of three-dimensional (3D) spatial measurement technologies are available and in development for facilitating machine vision. Among a multitude of applications, autonomous vehicles represent a rapidly-growing segment in need of effective and robust machine vision technology.
In the context of autonomous vehicle control, it is desirable to have a system recognize a variety of different objects that may be encountered, such as other vehicles, pedestrians, bicycles, trees, buildings, bridges and other infrastructure, as well as obstacles such as bumps and potholes, construction barriers, debris, animals, and the like. When an autonomous system recognizes different types of objects, their various behaviors may be predicted to enhance the safety and comfort of the autonomous vehicle.
Before objects can be recognized by a machine-vision system, they must first be detected from among their surroundings. This detection process is referred to as segmentation. There is an ongoing need to improve the performance and computational efficiency of the segmentation operations to provide more effective, reliable, and affordable machine vision solutions.